Zhanzhong Gu, Xiangjian He, Ping Yu, Wenjing Jia, Xiguang Yang, Gang Peng, Penghui Hu, Shiyan Chen, Hongjie Chen, Yiguang Lin
BACKGROUND: Stroke is a prevalent disease with a significant global impact. Effective assessment of stroke severity is vital for an accurate diagnosis, appropriate treatment, and optimal clinical outcomes. The National Institutes of Health Stroke Scale (NIHSS) is a widely used scale for quantitatively assessing stroke severity. However, the current manual scoring of NIHSS is labor-intensive, time-consuming, and sometimes unreliable. Applying artificial intelligence (AI) techniques to automate the quantitative assessment of stroke on vast amounts of electronic health records (EHRs) has attracted much interest...
April 2024: Artificial Intelligence in Medicine